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At least 1,261 records · Page 70

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DOE OSTI2025

Life-Cycle Cost Analysis Framework for Water Efficiency Measures

Life-Cycle Cost Analysis Framework for Water Efficiency Measures: Guidance Designed for Federal Agencies (hereafter referred to as “this report”) provides a technical framework for federal agencies to conduct a life-cycle cost analysis (LCCA) for water efficiency projects in accordance with 42 U.S.C. § 8253. This report leverages insights from the 2023 report, PNNL-34006, Water and Wastewater Annual Price Escalation Rates for Selected Cities Across the United States: 2023 Edition (Unger et al. 2023). The primary objective of this report is to provide a framework to assist federal agencies with evaluating the full economic impact of water efficiency projects by assessing both initial investments and long-term operational benefits. An LCCA can provide a comprehensive view of all costs associated with a water efficiency project, including initial investment, ongoing operations and maintenance (O&M), and eventual disposal or replacement, ensuring the most cost-effective solution is selected. The LCCA methodology outlined in this report enables users to compare base case scenarios with potential alternatives using a standardized present value approach. It incorporates key cost components such as energy, water and wastewater, installation, O&M, and equipment replacement. Additionally, the framework introduces relevant evaluation metrics, such as the net savings and the savings-to-investment ratio, to ensure that water efficiency measures are economically justified over the lifespan of the project. To support practical application, this report also describes various water efficiency strategies that may be analyzed using an LCCA, including plumbing retrofits, irrigation upgrades, alternative water use, and cooling system improvements. By applying this framework, federal agencies can ensure compliance with regulatory mandates while maximizing the return on investment and contributing to resilient water management practices. The information provided in this report is aligned with the Federal Energy Management Program (FEMP) life-cycle cost (LCC) methodology, as conveyed in National Institute of Standards and Technology (NIST) Handbook 135 (Kneifel and Webb 2022). This report is intended to provide relatively high-level guidance, acting as a complement to, rather than a substitute for, that resource.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Spinel Cu–Mn–Cr Oxide Nanoparticle-Pigmented Solar Selective Coatings Maintaining >94% Efficiency at 750 °C

High-temperature concentrating solar power (CSP) system is capable of harvesting and storing solar energy as heat towards cost-effective dispatchable solar electricity. Solar selective coating is a critical component to boost its efficiency by maximizing solar absorptance and minimizing thermal emittance losses. However, maintaining a high solar-thermal conversion efficiency >90% for long-term operation at ≥750 ºC remains a significant challenge. Herein, we report spray-coated spinel Cu-Mn-Cr oxide nanoparticle-pigmented solar selective coatings on Inconel tube sections maintaining ≥94% efficiency at 750 ºC and ≥92.5% at 800 ºC under 1000x solar concentration after 60 simulated day-night thermal cycles in air, each cycle comprising 12h at 750 ºC/800 ºC and 12h cooling to 25 ºC. The solar spectral selectivity is intrinsic to the band-to-band and d-d transitions of non-stoichiometric spinel Cu-Mn-Cr oxide nanoparticles. Furthermore, this feature offers a large fabrication tolerance in nanoparticle volume fraction and coating thickness, facilitating low-cost and scalable spray-coated high-efficiency solar selective absorbers for high-temperature CSP systems.

14 SOLAR ENERGY↗

DOE OSTI2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LCOH).

Giridhar, Nishant↗

DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

Giridhar, Nishant↗

DOE OSTI2025

Integrating Energy-Efficient Computing with Computational Research to Accelerate Energy Technology

NREL's computational sciences center hosts the largest high performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of operation, HPC use in EERE-sponsored research has grown by a factor of 30, including work in electricity generation, energy efficiency, transportation, and energy system modeling. This paper analyzes this research portfolio, providing examples of individual use cases. The paper documents NREL's history of operating one of the world's most energy-efficient data centers while examining pathways to reduce economic and environmental impact beyond reduction of Power Usage Efficiency (PUE). This paper concludes by examining the unique opportunities created for accelerating improvements in data center efficiency created by combining an HPC system dedicated to energy research and a research program in energy-efficient computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Integrated halide perovskite photoelectrochemical cells with solar-driven water-splitting efficiency of 20.8%

Abstract Achieving high solar-to-hydrogen (STH) efficiency concomitant with long-term durability using low-cost, scalable photo-absorbers is a long-standing challenge. Here we report the design and fabrication of a conductive adhesive-barrier (CAB) that translates >99% of photoelectric power to chemical reactions. The CAB enables halide perovskite-based photoelectrochemical cells with two different architectures that exhibit record STH efficiencies. The first, a co-planar photocathode-photoanode architecture, achieved an STH efficiency of 13.4% and 16.3 h to t 60 , solely limited by the hygroscopic hole transport layer in the n-i-p device. The second was formed using a monolithic stacked silicon-perovskite tandem, with a peak STH efficiency of 20.8% and 102 h of continuous operation before t 60 under AM 1.5G illumination. These advances will lead to efficient, durable, and low-cost solar-driven water-splitting technology with multifunctional barriers.

08 HYDROGEN↗

DOE OSTI2023

Lowering the operating temperature of protonic ceramic electrochemical cells to <450 °C

Protonic ceramic electrochemical cells (PCECs) can be employed for power generation and sustainable hydrogen production. Lowering the PCEC operating temperature can facilitate its scale-up and commercialization. However, achieving high energy efficiency and long-term durability at low operating temperatures is a long-standing challenge. Here, in this work, we report a simple and scalable approach for fabricating ultrathin, chemically homogeneous, and robust proton-conducting electrolytes and demonstrate an in situ formed composite positive electrode, Ba 0.62 Sr 0.38 CoO 3–δ –Pr 1.44 Ba 0.11 Sr 0.45 Co 1.32 Fe 0.68 O 6–δ , which significantly reduces ohmic resistance, positive electrode–electrolyte contact resistance and electrode polarization resistance. The PCECs attain high power densities in fuel-cell mode (~0.75 W cm –2 at 450 °C and ~0.10 W cm –2 at 275 °C) and exceptional current densities in steam electrolysis mode (–1.28 A cm –2 at 1.4 V and 450 °C). At 600 °C, the PCECs achieve a power density of ~2 W cm –2 . Additionally, we demonstrate the direct utilization of methane and ammonia for power generation at <450 °C. Our PCECs are also stable for power generation and hydrogen production at 400 °C.

25 ENERGY STORAGE↗

DOE OSTI2023

High Inlet Temperature Combustor for Direct-Fired Supercritical Oxy-Combustion

It is envisioned that supercritical CO 2 (sCO 2 ) electric power plant efficiencies can exceed 52% with 99% carbon capture using direct-fired oxy-combustion. Such efficiencies would be competitive with Natural Gas Combined Cycles (NGCC) and operate with nearly zero carbon emissions. To achieve high plant efficiencies, turbine inlet conditions must approach 1,200 °C at 250 bar. Such conditions, while desirable from a systems perspective, exceed the current state of the art in turbine design and materials qualification. The team of Southwest Research Institute ® (SwRI ® ), Georgia Institute of Technology (Georgia Tech), Spectral Energies, LLC (Spectral), GE Global Research (GE-GRC), and the University of Central Florida (UCF) sought to demonstrate an intermediate step toward these high efficiency plants with the development of a 1 MWth, subscale direct-fired oxy-fuel combustor. The efforts focused largely on the development of an auto-ignition based combustion system capable of providing the targeted 1200 °C and the pilot-scale plant to operate the combustor. While the project was stopped short of the commissioning and demonstration work, the advances made in this effort will reduce risks associated with chemical kinetics, thermal management, water separation, flue gas cleanup, materials selection, and corrosion in future demonstration efforts for the direct-fired oxy-fuel combustion cycles. The following report documents the design, analysis, and installation efforts completed during the project periods of performance.

03 NATURAL GAS↗

DOE OSTI2023

Enhancing the Faradaic efficiency of solid oxide electrolysis cells: progress and perspective

Abstract To reduce global warming, many countries are shifting to sustainable energy production systems. Solid oxide electrolysis cells (SOECs) are being considered due to their high hydrogen generation efficiency. However, low faradaic efficiency in scaling SOEC technology affects costs and limits large-scale adoption of hydrogen as fuel. This review covers SOECs’ critical aspects: current state-of-the-art anode, cathode, and electrolyte materials, operational and materials parameters affecting faradaic efficiency, and computational modeling techniques to resolve bottlenecks affecting SOEC faradaic efficiency.

Gaikwad, Prashik S. (ORCID:0000000179510115)↗

DOE OSTI2025

A Linear-Complexity Tensor Butterfly Algorithm for Compressing High-Dimensional Oscillatory Integral Operators

This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extension of the so-called complementary low-rank property of existing matrix butterfly algorithms. The algorithm partitions the discretized integral operator tensor into subtensors of multiple levels and factorizes each subtensor at the middle level as a Tucker-type interpolative decomposition, whose factor matrices are formed in a multilevel fashion. For a d-dimensional (d > 1) integral operator discretized into a 2d-mode tensor with n2d entries, the overall CPU time and memory requirement scale as O(nd), in stark contrast to the O(nd log n) complexity of existing matrix algorithms such as matrix butterfly algorithms and fast Fourier transforms (FFTs), where n is the number of points per direction. When comparing with other tensor algorithms such as quantized tensor train (QTT), the proposed algorithm also shows superior CPU and memory performance for tensor contraction. Remarkably, the tensor butterfly algorithm can efficiently model high-frequency Green's function interactions between two unit cubes, each spanning 512 wavelengths per direction, which represents problems of scale over 512× larger than that existing butterfly algorithms can handle, with the same amount of computation resources. On the other hand, for a problem representing 64 wavelengths per direction, which is the largest size existing algebraic matrix algorithms can handle, our tensor butterfly algorithm exhibits 200x speedups and 30× memory reduction compared with existing ones. Moreover, the tensor butterfly algorithm also permits O(nd)-complexity FFTs and Radon transforms up to d = 6 dimensions.

Kielstra, P Michael↗

DOE OSTI2023

Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.

distributed energy resources aggregation↗

DOE OSTI2023

Low-temperature solar thermal-power systems for residential electricity supply under various seasonal and climate conditions

In this work, the performance of low-temperature (<100 degrees C) solar thermal-power systems to satisfy residential electric loads was analyzed. The solar-driven system was designed to provide a fraction of the total electricity demand in a complementary operation with the electric grid. The analysis was conducted for an coperation during seven days each season, considering real solar and climate variables and residential loads at different climate zones in the United States. The efficiency of the system strongly depends on the solar radiation profile and the ambient temperature. Maximum efficiencies of around 9.5% were obtained in the cold and marine climate zones due to the high solar energy input and low heat dissipation temperatures. In these two zones, the system could supply more than 98% of the electricity demand all seasons. At mixed-humid and hot-humid regions, the system supplied around 50% of the electric load in three of the four seasons, but it only supplies about 27% of the electricity needs in the mixed-humid zone during summer and hot-humid zone during winter. The effect of the solar collector field area and the tank volume was also analyzed. In general, larger solar fields positively impact the efficiency. However, the impact of the tank volume varies depending on the solar radiation profile and the load requirements. Average efficiencies for the seven-day operation can be larger than 6% with a proper selection of the solar collector area and tank volume for an Organic Rankine Cycle with a capacity of about 2.6 kW. Finally, an economic analysis of the system was conducted, and the results were compared with a solar PV + battery system of similar capacity. It is expected that the cost for the analyzed solar-thermal system decreases in the coming years with the increased interest on low temperature applications.

14 SOLAR ENERGY↗

DOE OSTI2023

Arithmetic Primitives for Efficient Neuromorphic Computing

Neuromorphic computing is steadily gaining popularity in many scientific and engineering disciplines. However, one of the biggest problems that has prevented widespread usage of neuromorphic computing is the lack of efficient encoding methods. Traditional encoding methods such as binning, rate encoding, and temporal encoding are based on unary encoding and generate a large number of spikes for certain applications, making them less energy efficient. Lack of better encoding methods has also prevented preprocessing operations from being carried out on neuromorphic computers. As a result, over 99% of the time can be spent on data preprocessing and data transfer operations in some cases, leading to an inefficient workflow. In this paper, we present preliminary results that would enable us to efficiently encode data and perform basic arithmetic operations on neuromorphic computers. First, we present a neuromorphic approach for the two’s complement encoding of numbers and leverage it to devise addition and multiplication circuits, which could be used in preprocessing operations on neuromorphic computers. We test our approach on the SuperNeuroMAT simulator. Our results indicate that two’s complement is a highly efficient encoding method in terms of time, space, and energy complexity and that the addition and multiplication circuits produce accurate results on two numbers having arbitrary precision.

Wurm, Ahna↗

DOE OSTI2024

Mineralization of alkaline waste for CCUS

Ex-situ mineralization processes leverage the reaction of alkaline materials with CO 2 to form solid carbonate minerals for carbon capture, utilization, and storage. Annually, enough alkaline waste is generated to reduce global CO 2 emissions by a significant percentage via mineralization. However, while the reaction is thermodynamically favorable and occurs spontaneously, it is kinetically limited. Thus, a number of techniques have emerged to increase the efficiency of mineralization to achieve a scalable process. In this review, we discuss mineralization of waste streams with significant potential to scale to high levels of CO 2 sequestration. Focus is placed on the effect of operating parameters on carbonation kinetics and efficiency, methods, cost, and current scale of technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Sustainable Energy Solutions for Thermal Load in Buildings—Role of Heat Pumps, Solar Thermal, and Hydrogen-Based Cogeneration Systems

Economic and population growth is leading to increased energy demand across all sectors—buildings, transportation, and industry. Adoption of new energy consumers such as electric vehicles could further increase this growth. Sensible utilization of clean renewable energy resources is necessary to sustain this growth. Thermal needs in a building pose a significant challenge to the energy infrastructure. Potential technological solutions to address growing energy demand while simultaneously lowering the carbon footprint and enhancing the grid flexibility are presented in this study. Performance assessment of heat pumps, solar thermal collectors, nonfossil fuel-based cogeneration systems, and their hybrid configurations is reported in this study. The impact of design configuration, coefficient of performance (COP), electric grid’s primary energy efficiency on the key attributes of total carbon footprint, life cycle costs, operational energy savings, and site-specific primary energy efficiency are analyzed and discussed in detail. In conclusion, heat pumps and hydrogen-fueled solid oxide fuel cells (SOFCs) are highly effective building energy resources compared to traditional approaches; however, the carbon intensity of electrical energy and hydrogen production are keys to the overall environmental benefit.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

High-Temperature Heat Transfer Fluid Circulator for Concentrating Solar Power Systems

A high temperature Heat Transfer Fluid circulator compressor with wide turn down ratio aerodynamic performance was designed, fabricated and tested for application with high pressure super Critical CO2 fluid capable of temperatures and pressures to >750°C and 25 MPa respectively. The modular system decoupled the drive motor system from an independent compressor shaft to provide thermal isolation between the high temperature compressor shaft and the lower temperature capable motor elements. While the thermal management approach appears to be viable there are potential losses in the secondary cooling flows that need to be taken into account and fine-tuned during operation in order to achieve desired overall system efficiencies. The system was also shown to be viable and dynamically stable throughout the entire operating speed range, thereby validating the overall system configuration and structural design approach used. Finally, while it was not possible to achieve specific PTC-10 test points, the compressor performance tests provide sufficient data to indicate that the compressor has the ability to achieve the required performance in practice. The two key factors affecting compressor design and cost are the maximum expected temperature and pressure. The maximum allowable temperatures for current state-of-the-art (SOA) motor driven compressor technology is limited to of approximately 150-200°C, but inlet temperatures >750 °C may be encountered. While the optimal efficiency of compression is in the transcritical region, some concepts require inlet pressures to 225 bar.

14 SOLAR ENERGY↗

DOE OSTI2021

High-Temperature Heat Transfer Fluid Circulator for Concentrating Solar Power Systems How Mohawk and Velo3Dare Printing the Future of Energy

14 SOLAR ENERGY↗